Dynamic Modeling of pH in a Dual-Tower Desulfurization System Based on an Improved Particle Swarm Optimization Algorithm

The dual-tower desulfurization system exhibits characteristics of significant delay and hysteresis in slurry concentration and pH value, which leads to certain deficiencies in the identification of slurry pH value. This paper analyzes the structure of a dual-tower desulfurization system in a power plant, taking the operational data under 590MW condition as an example, to conduct data analysis on factors affecting the slurry pH value. A closed-loop identification method is employed, utilizing an improved particle swarm optimization algorithm for parameter optimization, to establish the transfer functions of inlet SO2 concentration, inlet flue gas flow, slurry supply volume, and slurry pH value. Simulation analysis is carried out through MATLAB, and the results show that the identification error of the front tower is controlled within 2.5%, while the identification accuracy of the rear tower is relatively higher, controlled below 0.4%. The identification function can closely follow the changes in the actual curve, proving the reliability of the identification model.